.. _l-onnx-op-dequantizelinear-d13-19:

DequantizeLinear - 13 vs 19
===========================

Next section compares an older to a newer version of the same operator 
after both definition are converted into markdown text.
Green means an addition to the newer version, red means a deletion.
Anything else is unchanged.

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    --- a/DequantizeLinear13
    +++ b/DequantizeLinear19
    @@ -1 +1 @@
      The linear dequantization operator. It consumes a quantized tensor, a scale, and a zero point to compute the full precision tensor.
      The dequantization formula is y = (x - x_zero_point) * x_scale. x_scale and x_zero_point must have same shape, and can be either a scalar
      for per-tensor / per layer quantization, or a 1-D tensor for per-axis quantization.
      x_zero_point and x must have same type. x and y must have same shape. In the case of dequantizing int32,
      there's no zero point (zero point is supposed to be 0).
    + zero-point is usually not used in the case of float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz quantization,
    + but the dequantization formula remains the same for consistency and 'x_scale' still determines the output type.
  
      ### Attributes
  
      * **axis - INT** (default is '1'):
  
    -   (Optional) The axis of the dequantizing dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
    +   (Optional) The axis of the dequantizing dimension of the input tensor. Used only for per-axis quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input). When the rank of the input is 1, per-tensor quantization is applied, rendering the axis unnecessary in this scenario.
  
      ### Inputs
  
      Between 2 and 3 inputs.
  
    - - **x** (heterogeneous) - **T**:
    + - **x** (heterogeneous) - **T1**:
    ?                              +
  
        N-D quantized input tensor to be de-quantized.
    - - **x_scale** (heterogeneous) - **tensor(float)**:
    ?                                   ^^^^^^^^^^^^^
    + - **x_scale** (heterogeneous) - **T2**:
    ?                                   ^^
  
        Scale for input 'x'. It can be a scalar, which means a per-tensor/layer dequantization, or a 1-D tensor for per-axis dequantization.
    - - **x_zero_point** (optional, heterogeneous) - **T**:
    + - **x_zero_point** (optional, heterogeneous) - **T1**:
    ?                                                   +
  
        Zero point for input 'x'. Shape must match x_scale. It's optional. Zero point is 0 when it's not specified.
  
      ### Outputs
  
    - - **y** (heterogeneous) - **tensor(float)**:
    ?                             ^^^^^^^^^^^^^
    + - **y** (heterogeneous) - **T2**:
    ?                             ^^
  
        N-D full precision output tensor. It has same shape as input 'x'.
  
      ### Type Constraints
  
    - * **T** in ( tensor(int32), tensor(int8), tensor(uint8) ):
    + * **T1** in ( tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int32), tensor(int8), tensor(uint8) ):
  
    -   Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.+   Constrain 'x_zero_point' and 'x' to 8-bit integer or float, or /32-bit integer tensor.
    ?                                            ++++++++++++++++++++++                       +
    + * **T2** in ( tensor(bfloat16), tensor(float), tensor(float16) ):
    + 
    +   'x_scale' determines the output type.
    `;

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